K.H. Beard | Search USGS

USGS2016

Interactions among vegetation, climate, and herbivory control greenhouse gas fluxes in a subarctic coastal wetland

High-latitude ecosystems are experiencing the most rapid climate changes globally, and in many areas these changes are concurrent with shifts in patterns of herbivory. Individually, climate and herbivory are known to influence biosphere-atmosphere greenhouse gas (GHG) exchange; however, the interactive effects of climate and herbivory in driving GHG fluxes have been poorly quantified, especially in coastal systems that support large populations of migratory waterfowl. We investigated the magnitude and the climatic and physical controls of GHG exchange within the Yukon-Kuskokwim Delta in western Alaska across four distinct vegetation communities formed by herbivory and local microtopography. Net CO 2 flux was greatest in the ungrazed Carex meadow community (3.97 ± 0.58 [SE] µmol CO 2 m −2 s −1 ), but CH 4 flux was greatest in the grazed community (14.00 ± 6.56 nmol CH 4 m −2 s −1 ). The grazed community is also the only vegetation type where CH 4 was a larger contributor than CO 2 to overall GHG forcing. We found that vegetation community was an important predictor of CO 2 and CH 4 exchange, demonstrating that variation in regional gas exchange is best explained when the effect of grazing, determined by the difference between grazed and ungrazed communities, is included. Further, we identified an interaction between temperature and vegetation community, indicating that grazed regions could experience the greatest increases in CH 4 emissions with warming. These results suggest that future GHG fluxes could be influenced by both climate and by changes in herbivore population dynamics that expand or contract the vegetation community most responsive to future temperature change.

Alaska↗

USGS2007

Random forests for classification in ecology

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Ecology↗